10 AI, Data Science, Machine Learning Terms You Need to Know in 2020

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• Session 1

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• Planning to make a career in the field of data
  science, machine learning, or artificial
  intelligence, well, there is no better time than
  today. There are a number of AI certificate
  programs, ML certification, and data science
  certification programs that will help you get
  acquainted with complete information about these
  technologies and its implementation.
• As a part of your data science training program,
  or AI certificate programs, or even in machine
  learning, it becomes essential that you must
  learn some of the common terminologies that you
  will come across during your certification in
  Artificial Intelligence, machine learning, and
  data science program.

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Key terms that you must know as AI, ML, and data
science expert

• AutoML- Or Automated Machine Learning. It is the
  process of automating the process of applying the
  concepts of machine learning to real-world
  problems. As a part of machine learning for
  beginners, you will cover concepts like algorithm
  architecture structure, data pre-processing,
  feature engineering, algorithm selection under
  AutoML.
• Bayesian- If you are planning to pursue
  certification in ML, then it's important to know
  about the statistical tools. One such tool is
  Bayesian, which also plays a key role in decision
  making. It belongs to the category of evidential
  probabilities as a part of this method, we study
  the probability of a hypothesis. This methodology
  stipulates a prior probability.

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• Data Engineer- They are responsible for
  optimizing and managing the storage and retrieval
  of an organization's data. They set a road map on
  how to acquire the data and form a database for
  storing it. These engineers deal with cloud
  services to optimize data storage and form
  algorithms that will be helping drawing
  inferences from the data. A data engineer
  requires knowledge in SQL, computer science, and
  database design.
• Deepfakes- These are fake images, videos, or
  audio created using Deep Learning and Generative
  Adversarial Networks technology. This is a very
  real technology which gives a more apt result.
  Initially, it became popular where the popular
  celebrities' photos were morphed in adult videos.
  This was misused in Sep 2019, when a company was
  scammed 243K wherein fake CEO's voice was used.

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• MLOps AIOps- MLOps is the implementation of the
  basic practices for developing and deploying
  machine learning models via effective
  collaboration with data scientists and IT
  professionals. Extending the above term, AIOps
  revolves around leveraging AI into the operations
  of a company. It incorporates machine learning
  technologies that derive useful information from
  IT systems. This methodology involves the
  combination of human intelligence with the AI
  algorithm, which enables IT teams to make a more
  formed decision.
• Natural language processing- It is important for
  learning Artificial Intelligence certification
  programs. Here the AI is trained to comprehend
  human communication. This forms the basis for
  various services like chatbots and translational
  services. Siri and Alexa are an example of the
  same.

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• Clickstream analytics- If you are willing to
  become a data science expert, then knowing about
  certain terminology is important. Clickstream
  analytics is the analysis of the way humans
  interact with machines, like recording and then
  analysis of where a mouse is clicked on a screen.
  This is just one of the examples of clickstream
  analysis, it basically is a measure of manual
  operation of machinery.
• Decision Trees- As a data science expert, you
  would need to know about decision trees. It is
  used by computers to analyze and classify the
  information. The working is simple a series of
  questions about a particular data is fed into the
  system, and then it is channelled as different
  branches into different outputs.

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• Transfer learning- As a part of AI certificate
  programs, you need to learn about different
  processes of AI and how the system works. One
  such concept is transfer learning. The simplest
  way to explain this is that when AI has learned
  about a certain program, it will keep on building
  its knowledge base on it, even if you haven't
  asked the machine to learn about the same. For
  example, if AI can determine, an image is a cat
  or not. It will continue to learn the same. Let's
  say you take an AI, and it spends a week
  identifying shoes after that, it can return back
  to its work on cats with great improvements and
  accuracy
• Reinforcement learning- It is a type of machine
  learning algorithm where the agency decides what
  would be the right course of action based on
  previous study. It learns optimal actions and
  works on trial and error.

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What's next?

• Data science, AI, and Machine Learning are wider
  terms, and it involves wider terminologies and
  uses cases. To have a complete understanding of
  how each of these technologies work, then you
  need to go for data science training, ml
  certification, and certification in artificial
  intelligence. Global Tech Council offers industry
  standards certification in artificial
  intelligence, ML, and Data Science. Once you have
  completed the certification program, you become
  eligible to implement the concepts in the
  practical field.

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• You can check out our certifications, and kick
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• Learn more about GlobalTech Council click here

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